Conventional approaches to geological reservoir modeling rely on three-dimensional Cartesian grids that can be iterated over time (e.g., to provide a four-dimensional model). A reservoir may span hundreds of square kilometers and be located kilometers in depth. The expansive nature of a typical oil reservoir brings various types of physical phenomena into play. Such phenomena may exhibit macroscale, microscale or a combination of macro- and microscale behavior. However, attempts to capture microscale phenomena via increased grid density or grid densities causes an increase in computational and other resource requirements. For example, increasing two-dimensional grid density by decreasing grid spacing from 10 meters by 10 meters to 5 meters by 5 meters will increase computational requirements significantly (e.g., a four-fold increase). Accordingly, most conventional models sacrifice microscale accuracy to maintain reasonable resource requirements. Various techniques described herein can allow for more accurate modeling of microscale phenomena (e.g., one meter resolution or less) without necessarily increasing grid density.